Examining site effects in gadgetron volumes (v3)
Virgilio Gonzenbach
2022-07-14
Data summary
Raw Data Tally
We are missing 3 raw data files in total that we should have:
Note: subject 03-001 was not scanned at NIH site; subject 03-002 was not scanned at Penn.
Total visits per subjects
Scans at site per subject
Missing segmentations
Showing only segmentation types with at least 1 file missing.
Segmentation volumetrics
Site effects
Paired t-tests didnot find a significant difference.
## [[1]]
##
## Paired t-test
##
## data: JLF_WM by recon
## t = 0.0081621, df = 10, p-value = 0.9936
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.2434550 0.2452452
## sample estimates:
## mean difference
## 0.0008951038
##
##
## [[2]]
##
## Paired t-test
##
## data: JLF_GM by recon
## t = -1.1724, df = 10, p-value = 0.2682
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.29414720 0.09132281
## sample estimates:
## mean difference
## -0.1014122
##
##
## [[3]]
##
## Paired t-test
##
## data: FIRST_thal by recon
## t = 0.28004, df = 10, p-value = 0.7852
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.2326935 0.2995922
## sample estimates:
## mean difference
## 0.03344938
##
##
## [[4]]
##
## Paired t-test
##
## data: JLF_thal by recon
## t = -0.42861, df = 10, p-value = 0.6773
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.3362662 0.2277673
## sample estimates:
## mean difference
## -0.05424947
##
##
## [[5]]
##
## Paired t-test
##
## data: mimosa by recon
## t = 1.0647, df = 10, p-value = 0.312
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.1516548 0.4292237
## sample estimates:
## mean difference
## 0.1387844
##
##
## [[6]]
##
## Paired t-test
##
## data: FAST_TBV by recon
## t = -2.3734, df = 10, p-value = 0.03905
## alternative hypothesis: true mean difference is not equal to 0
## 95 percent confidence interval:
## -0.42142807 -0.01330599
## sample estimates:
## mean difference
## -0.217367Permutation Testing
Gadgetron Volumes
pseudo-F Ratio statistics: Intersite Variability / Intrasite Variability
Distribution of permutated pseudo-F stats:
P-value of pseudo-F stat:
## JLF_WM JLF_GM FIRST_thal JLF_thal mimosa FAST_TBV
## 0.00609939 0.05309469 0.01519848 0.16568343 0.00289971 0.05419458Onscanner Volumes
pseudo-F statistics: Intersite Variability / Intrasite Variability
Distribution of permutated pseudo-F stats:
P-value of pseudo-F stat:
## JLF_WM JLF_GM FIRST_thal JLF_thal mimosa FAST_TBV
## 0.00009999 0.00189981 0.07659234 0.00889911 0.05389461 0.00009999Mixed Models
## $JLF_WM
## Linear mixed model fit by REML ['lmerMod']
## Formula: JLF_WM ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 472.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0446 -0.2112 0.1467 0.3164 2.1308
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 34.87 5.905
## ID (Intercept) 502.56 22.418
## Residual 58.55 7.652
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 510.142 6.918 73.75
##
## $JLF_GM
## Linear mixed model fit by REML ['lmerMod']
## Formula: JLF_GM ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15565 -0.46933 0.02882 0.29009 2.96851
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 92.53 9.619
## ID (Intercept) 971.22 31.164
## Residual 70.74 8.411
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 712.946 9.622 74.1
##
## $FIRST_thal
## Linear mixed model fit by REML ['lmerMod']
## Formula: FIRST_thal ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 298.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7565 -0.0293 0.1583 0.3316 1.9379
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 3.369 1.835
## ID (Intercept) 1.833 1.354
## Residual 5.158 2.271
## Number of obs: 60, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 12.9406 0.6042 21.42
##
## $JLF_thal
## Linear mixed model fit by REML ['lmerMod']
## Formula: JLF_thal ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 125.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8226 -0.2199 0.0098 0.3410 1.3244
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 0.04114 0.2028
## ID (Intercept) 1.28938 1.1355
## Residual 0.22288 0.4721
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 13.5394 0.3499 38.7
##
## $mimosa
## Linear mixed model fit by REML ['lmerMod']
## Formula: mimosa ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 475.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54186 -0.32830 -0.01482 0.47758 1.61987
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 84.74 9.206
## ID (Intercept) 62.42 7.900
## Residual 86.85 9.319
## Number of obs: 60, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 38.917 3.154 12.34
##
## $FAST_TBV
## Linear mixed model fit by REML ['lmerMod']
## Formula: FAST_TBV ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 628.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5039 -0.1621 0.1276 0.4302 2.7070
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 9.391e-11 9.691e-06
## ID (Intercept) 1.953e+03 4.419e+01
## Residual 1.349e+03 3.673e+01
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1028.74 14.16 72.64
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')ICC of Subject Random Effect
ICC of Site:Subject Random Effect
Mixed model Diagnostics
FAST TBV
## $summary
## Linear mixed model fit by REML ['lmerMod']
## Formula: FAST_TBV ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 628.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5039 -0.1621 0.1276 0.4302 2.7070
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 9.391e-11 9.691e-06
## ID (Intercept) 1.953e+03 4.419e+01
## Residual 1.349e+03 3.673e+01
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1028.74 14.16 72.64
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
##
##
## $perf
## [1] NA
##
## $shapiro.resid
##
## Shapiro-Wilk normality test
##
## data: residuals(model)
## W = 0.78891, p-value = 6.012e-08
##
##
## $qq.resid
## [1] 61 60
##
## $shapiro.ID
##
## Shapiro-Wilk normality test
##
## data: coef(model)$ID[, 1]
## W = 0.91642, p-value = 0.29
##
##
## $`shapiro.site:ID`
##
## Shapiro-Wilk normality test
##
## data: coef(model)$`site:ID`[, 1]
## W = 0.90285, p-value = 0.008495
##
##
## $qq.ranef.ID
## [1] 6 8
##
## $`qq.ranef.site:ID`
## [1] 24 25
##
## $res.v.fit
JLF GM
## $summary
## Linear mixed model fit by REML ['lmerMod']
## Formula: JLF_GM ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 498.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.15565 -0.46933 0.02882 0.29009 2.96851
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 92.53 9.619
## ID (Intercept) 971.22 31.164
## Residual 70.74 8.411
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 712.946 9.622 74.1
##
## $perf
## # ICC by Group
##
## Group | ICC
## ---------------
## site:ID | 0.082
## ID | 0.856
##
## $shapiro.resid
##
## Shapiro-Wilk normality test
##
## data: residuals(model)
## W = 0.9448, p-value = 0.008218
##
##
## $qq.resid
## [1] 7 9
##
## $shapiro.ID
##
## Shapiro-Wilk normality test
##
## data: coef(model)$ID[, 1]
## W = 0.65902, p-value = 0.0001402
##
##
## $`shapiro.site:ID`
##
## Shapiro-Wilk normality test
##
## data: coef(model)$`site:ID`[, 1]
## W = 0.9114, p-value = 0.01404
##
##
## $qq.ranef.ID
## [1] 6 9
##
## $`qq.ranef.site:ID`
## [1] 13 2
##
## $res.v.fit
JLF WM
## $summary
## Linear mixed model fit by REML ['lmerMod']
## Formula: JLF_WM ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 472.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0446 -0.2112 0.1467 0.3164 2.1308
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 34.87 5.905
## ID (Intercept) 502.56 22.418
## Residual 58.55 7.652
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 510.142 6.918 73.75
##
## $perf
## # ICC by Group
##
## Group | ICC
## ---------------
## site:ID | 0.059
## ID | 0.843
##
## $shapiro.resid
##
## Shapiro-Wilk normality test
##
## data: residuals(model)
## W = 0.81664, p-value = 3.03e-07
##
##
## $qq.resid
## [1] 60 61
##
## $shapiro.ID
##
## Shapiro-Wilk normality test
##
## data: coef(model)$ID[, 1]
## W = 0.95364, p-value = 0.6907
##
##
## $`shapiro.site:ID`
##
## Shapiro-Wilk normality test
##
## data: coef(model)$`site:ID`[, 1]
## W = 0.95343, p-value = 0.194
##
##
## $qq.ranef.ID
## [1] 6 1
##
## $`qq.ranef.site:ID`
## [1] 31 21
##
## $res.v.fit
FIRST thal
## $summary
## Linear mixed model fit by REML ['lmerMod']
## Formula: FIRST_thal ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 298.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7565 -0.0293 0.1583 0.3316 1.9379
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 3.369 1.835
## ID (Intercept) 1.833 1.354
## Residual 5.158 2.271
## Number of obs: 60, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 12.9406 0.6042 21.42
##
## $perf
## # ICC by Group
##
## Group | ICC
## ---------------
## site:ID | 0.325
## ID | 0.177
##
## $shapiro.resid
##
## Shapiro-Wilk normality test
##
## data: residuals(model)
## W = 0.75021, p-value = 9.414e-09
##
##
## $qq.resid
## 24 18
## 23 17
##
## $shapiro.ID
##
## Shapiro-Wilk normality test
##
## data: coef(model)$ID[, 1]
## W = 0.92452, p-value = 0.3581
##
##
## $`shapiro.site:ID`
##
## Shapiro-Wilk normality test
##
## data: coef(model)$`site:ID`[, 1]
## W = 0.88576, p-value = 0.003239
##
##
## $qq.ranef.ID
## [1] 7 9
##
## $`qq.ranef.site:ID`
## [1] 24 25
##
## $res.v.fit
JLF thal
## $summary
## Linear mixed model fit by REML ['lmerMod']
## Formula: JLF_thal ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 125.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8226 -0.2199 0.0098 0.3410 1.3244
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 0.04114 0.2028
## ID (Intercept) 1.28938 1.1355
## Residual 0.22288 0.4721
## Number of obs: 61, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 13.5394 0.3499 38.7
##
## $perf
## # ICC by Group
##
## Group | ICC
## ---------------
## site:ID | 0.026
## ID | 0.830
##
## $shapiro.resid
##
## Shapiro-Wilk normality test
##
## data: residuals(model)
## W = 0.70624, p-value = 9.497e-10
##
##
## $qq.resid
## [1] 17 60
##
## $shapiro.ID
##
## Shapiro-Wilk normality test
##
## data: coef(model)$ID[, 1]
## W = 0.95519, p-value = 0.7106
##
##
## $`shapiro.site:ID`
##
## Shapiro-Wilk normality test
##
## data: coef(model)$`site:ID`[, 1]
## W = 0.88676, p-value = 0.003423
##
##
## $qq.ranef.ID
## [1] 1 9
##
## $`qq.ranef.site:ID`
## [1] 24 14
##
## $res.v.fit
Mimosa
## $summary
## Linear mixed model fit by REML ['lmerMod']
## Formula: mimosa ~ (1 | ID) + (1 | site:ID)
## Data: vol_df
##
## REML criterion at convergence: 475.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54186 -0.32830 -0.01482 0.47758 1.61987
##
## Random effects:
## Groups Name Variance Std.Dev.
## site:ID (Intercept) 84.74 9.206
## ID (Intercept) 62.42 7.900
## Residual 86.85 9.319
## Number of obs: 60, groups: site:ID, 31; ID, 11
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 38.917 3.154 12.34
##
## $perf
## # ICC by Group
##
## Group | ICC
## ---------------
## site:ID | 0.362
## ID | 0.267
##
## $shapiro.resid
##
## Shapiro-Wilk normality test
##
## data: residuals(model)
## W = 0.96095, p-value = 0.05234
##
##
## $qq.resid
## 60 52
## 59 51
##
## $shapiro.ID
##
## Shapiro-Wilk normality test
##
## data: coef(model)$ID[, 1]
## W = 0.97072, p-value = 0.8937
##
##
## $`shapiro.site:ID`
##
## Shapiro-Wilk normality test
##
## data: coef(model)$`site:ID`[, 1]
## W = 0.92175, p-value = 0.02627
##
##
## $qq.ranef.ID
## [1] 1 9
##
## $`qq.ranef.site:ID`
## [1] 13 27
##
## $res.v.fit